A typical task assignment system algorithmically assigns tasks arriving at a task assignment center to agents available to handle those tasks. At times, the task assignment center may be in an “L1 state” and have agents available and waiting for assignment to tasks. At other times, the task assignment center may be in an “L2 state” and have tasks waiting in one or more queues for an agent to become available for assignment. At yet other times, the task assignment system may be in an “L3 state” and have multiple agents available and multiple tasks waiting for assignment. An example of a task assignment system is a contact center system that receives contacts (e.g., telephone calls, internet chat sessions, emails, etc.) to be assigned to agents.
In some typical task assignment centers, tasks are assigned to agents ordered based on time of arrival, and agents receive tasks ordered based on the time when those agents became available. This strategy may be referred to as a “first-in, first-out,” “FIFO,” or “round-robin” strategy. For example, in an L2 environment, when an agent becomes available, the task at the head of the queue would be selected for assignment to the agent.
In other typical task assignment centers, a performance-based routing (PBR) strategy for prioritizing higher-performing agents for task assignment may be implemented. Under PBR, for example, the highest-performing agent among available agents receives the next available task. Other PBR and PBR-like strategies may make assignments using specific information about the agents.
“Behavioral Pairing” or “BP” strategies, for assigning tasks to agents, improve upon traditional assignment methods. BP targets balanced utilization of agents while simultaneously improving overall task assignment center performance potentially beyond what FIFO or PBR methods will achieve in practice.
When determining a BP model for a BP strategy, a task assignment system may consider information about its agents and incoming tasks or types of tasks. For example, a contact center system may consider the performance history of each agent, such as an agent's conversion rate in a sales queue, and it may consider customer information about a contact, such as the type of service a customer uses or how many years the customer has had a contract with the company, and other types of data found in a typical customer relationship management (CRM) system.
In some contact center systems, it may be advantageous for a BP model to account for historical information associated with a contact's phone number (e.g., billing telephone number (BTN)). Thus, it may be understood that there may be a need for a BP model that takes into consideration historical information associated with a contact's phone number in order to optimize the overall performance of a contact center system.